Official PyTorch(Geometric) implementation of DPGNN(DPGCN) in "Distance-wise Prototypical Graph Neural Network for Node Imbalance Classification"

Yu Wang (Jack)

Last update: Oct 12, 2022

Related tags

Deep Learning DPGNN

Overview

DPGNN

This repository is an official PyTorch(Geometric) implementation of DPGNN(DPGCN) in "Distance-wise Prototypical Graph Neural Network for Node Imbalance Classification"

Requirements

PyTorch 1.8.1+cu111
PyTorch Geometric 1.7.0
Pytorch-scatter 2.0.7
NetworkX 2.5.1
Tqdm 4.61.0
Sklearn 0.0

Note that the version of PyTorch and PyTorch Geometric should be compatible and PyTorch Geometric is related to other packages, which requires to be installed beforehand. It is recommended to follow the installation instruction.

Run

To reproduce the performance comparison and the ablation study in the following Table and the Figure , run

bash run.sh

To reproduce our results under different imbalance ratio, run

bash run_ratio.sh

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Comments

Looking forward to your reply!!

Hello Dr. Wang, I am re-reading your paper intensively. I have encountered some problems in the explanation part of formula (9). I would like to ask you to solve confusion.

1.“computes the inverse ratio of the number of labeled nodes in the node 𝑣𝑖’s class 𝜙(𝑣𝑖),Í𝑣𝑗∈V𝑙Y𝑗𝜙(𝑣𝑖), to the total number of labeled nodes |V𝑙|.”My colleagues have different perceptions of this sentence, We want to know what exactly Í𝑣𝑗∈V𝑙Y𝑗𝜙(𝑣𝑖) stands for?

2.why |V𝑙| and Í𝑣𝑗∈V𝑙Y𝑗𝜙(𝑣𝑖) are inversely ratio, Shouldn't they be proportional when the weighting factors are given?

3.Then this part also talks about the network homogeneity can expand the training data, I would like to ask you whether it is achieved by formula 12?

Sorry to bother you, looking forward to your reply!!

opened by Jinn02-25 3
Dear author, I would like to ask a question about the data set？

In the process of running the code, I found that the DE, EN, ES.... I don’t know which data sets these names replace. Could you please answer in your busy schedule?

opened by Jinn02-25 6

Owner

Yu Wang (Jack)

A 1st year Ph.D. student in the Computer Science Department at Vanderbilt University. Research work: graph data mining, deep learning, network analysis

GitHub

Official PyTorch(Geometric) implementation of DPGNN(DPGCN) in "Distance-wise Prototypical Graph Neural Network for Node Imbalance Classification"

Related tags

Overview

DPGNN

Requirements

Run

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Comments

Looking forward to your reply!!

Dear author, I would like to ask a question about the data set？

Owner

Yu Wang (Jack)

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